Recurrent Neural Networks (RNNs) are a type of neural network architecture designed to process sequential data by maintaining internal state (memory) to capture temporal dependencies within the data. Unlike feedforward neural networks, which process each input independently, RNNs are capable of capturing information from previous time steps and using it to influence the processing of subsequent time steps.

The basic building block of an RNN is the recurrent neuron, which takes both the current input and the previous hidden state as input and produces an output and a new hidden state. This hidden state acts as a form of memory, allowing the network to retain information about previous inputs and use it to make predictions or generate outputs.

In Keras, implementing RNNs is straightforward using the SimpleRNN, LSTM (Long Short-Term Memory), or GRU (Gated Recurrent Unit) layers. Here's how you can implement an RNN in Keras:

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import SimpleRNN, LSTM, GRU, Dense

# Define the model architecture
model = Sequential()

# Add an RNN layer (e.g., SimpleRNN, LSTM, GRU)
model.add(SimpleRNN(units=64, input_shape=(time_steps, input_dim)))  # SimpleRNN
# model.add(LSTM(units=64, input_shape=(time_steps, input_dim)))  # LSTM
# model.add(GRU(units=64, input_shape=(time_steps, input_dim)))  # GRU

# Add a dense output layer
model.add(Dense(units=num_classes, activation='softmax'))

# Compile the model
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

# Print the model summary
model.summary()?

In the above code :

* We import the necessary modules from Keras.
* We define a sequential model using Sequential().
* We add an RNN layer (e.g., SimpleRNN, LSTM, or GRU) to the model using add(). Each of these layers takes the units parameter, which specifies the dimensionality of the output space, and the input_shape parameter, which specifies the shape of the input data.
* We add a dense output layer to the model using add(). This layer is typically used to produce the final predictions or outputs of the model.
* We compile the model using compile() with the appropriate optimizer, loss function, and metrics.
* We print the model summary using summary() to display the architecture of the model, including the number of parameters and output shapes of each layer.
* By using the appropriate RNN layer in Keras, you can easily build and train RNN architectures for various sequential data tasks such as time series prediction, natural language processing, and sequence generation.